Chemometric approaches to low-content quantification (LCQ) in solid-state mixtures using Raman mapping spectroscopy.
Ryder, Alan G.
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Li, Boyan, Casamayou-Boucau, Yannick, Calvet, Amandine, & Ryder, Alan G. (2017). Chemometric approaches to Low-content quantification (LCQ) in solid-state mixtures using Raman mapping spectroscopy. Analytical Methods. doi: 10.1039/C7AY01778B
The low-content quantification (LCQ) of active pharmaceutical ingredients or impurities in solid mixtures is important in pharmaceutical manufacturing and analysis. We previously demonstrated the feasibility of using Raman mapping of micro-scale heterogeneity of solid-state samples combined with partial least squares (PLS) regression for LCQ in a binary system.1 However, PLS is limited by the need for relatively high calibration sample numbers to attain high accuracy, and a rather significant computational time requirement for the large Raman maps. Here we evaluated alternative chemometric methods which might overcome these issues. The methods were: net analyte signal coupled with classical least squares (NAS-CLS), multivariate curve resolution (MCR), principal component analysis with CLS (PCA-CLS), and the ratio of characteristic analyte/matrix bands combined with shape-preserving piecewise cubic polynomial interpolation curve fitting (BR-PCHIP). For high (>1.0%) piracetam analyte content, all methods were accurate with relative errors of prediction (REP) of: